






"""simple docstring"""







import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class    lowerCamelCase__   (			__lowerCAmelCase					):
    SCREAMING_SNAKE_CASE						=     ['''image_processor''', '''tokenizer''']
    SCREAMING_SNAKE_CASE						=     '''LayoutLMv3ImageProcessor'''
    SCREAMING_SNAKE_CASE						=     ('''LayoutLMv3Tokenizer''', '''LayoutLMv3TokenizerFast''')






    def __init__(					self      ,A=None      ,A=None      ,**A   ):
          UpperCAmelCase							     =			None
          if "feature_extractor" in kwargs:
                warnings.warn(
                    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                    """ instead."""      ,_lowerCamelCase      ,)
                UpperCAmelCase							     =			kwargs.pop("""feature_extractor"""   )

          UpperCAmelCase							     =			image_processor if image_processor is not None else feature_extractor
          if image_processor is None:
                raise ValueError("""You need to specify an `image_processor`."""   )
          if tokenizer is None:
                raise ValueError("""You need to specify a `tokenizer`."""   )

          super().__init__(_lowerCamelCase      ,_lowerCamelCase   )






    def __call__(					self      ,A      ,A = None      ,A = None      ,A = None      ,A = None      ,A = True      ,A = False      ,A = None      ,A = None      ,A = 0      ,A = None      ,A = None      ,A = None      ,A = False      ,A = False      ,A = False      ,A = False      ,A = True      ,A = None      ,**A      ,):
          # verify input
          if self.image_processor.apply_ocr and (boxes is not None):
                raise ValueError(
                    """You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."""   )

          if self.image_processor.apply_ocr and (word_labels is not None):
                raise ValueError(
                    """You cannot provide word labels if you initialized the image processor with apply_ocr set to True."""   )

          # first, apply the image processor
          UpperCAmelCase							     =			self.image_processor(images=_lowerCamelCase      ,return_tensors=_lowerCamelCase   )

          # second, apply the tokenizer
          if text is not None and self.image_processor.apply_ocr and text_pair is None:
                if isinstance(_lowerCamelCase      ,_lowerCamelCase   ):
                      UpperCAmelCase							     =			[text]  # add batch dimension (as the image processor always adds a batch dimension)
                UpperCAmelCase							     =			features["""words"""]

          UpperCAmelCase							     =			self.tokenizer(
              text=text if text is not None else features["""words"""]      ,text_pair=text_pair if text_pair is not None else None      ,boxes=boxes if boxes is not None else features["""boxes"""]      ,word_labels=_lowerCamelCase      ,add_special_tokens=_lowerCamelCase      ,padding=_lowerCamelCase      ,truncation=_lowerCamelCase      ,max_length=_lowerCamelCase      ,stride=_lowerCamelCase      ,pad_to_multiple_of=_lowerCamelCase      ,return_token_type_ids=_lowerCamelCase      ,return_attention_mask=_lowerCamelCase      ,return_overflowing_tokens=_lowerCamelCase      ,return_special_tokens_mask=_lowerCamelCase      ,return_offsets_mapping=_lowerCamelCase      ,return_length=_lowerCamelCase      ,verbose=_lowerCamelCase      ,return_tensors=_lowerCamelCase      ,**_lowerCamelCase      ,)

          # add pixel values
          UpperCAmelCase							     =			features.pop("""pixel_values"""   )
          if return_overflowing_tokens is True:
                UpperCAmelCase							     =			self.get_overflowing_images(_lowerCamelCase      ,encoded_inputs["""overflow_to_sample_mapping"""]   )
          UpperCAmelCase							     =			images

          return encoded_inputs






    def 							_UpperCamelCase				(					self      ,A      ,A   ):
          # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
          UpperCAmelCase							     =			[]
          for sample_idx in overflow_to_sample_mapping:
                images_with_overflow.append(images[sample_idx]   )

          if len(_lowerCamelCase   ) != len(_lowerCamelCase   ):
                raise ValueError(
                    """Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"""
                    F''' {len(_lowerCamelCase   )} and {len(_lowerCamelCase   )}'''   )

          return images_with_overflow






    def 							_UpperCamelCase				(					self      ,*A      ,**A   ):
          return self.tokenizer.batch_decode(*_lowerCamelCase      ,**_lowerCamelCase   )






    def 							_UpperCamelCase				(					self      ,*A      ,**A   ):
          return self.tokenizer.decode(*_lowerCamelCase      ,**_lowerCamelCase   )






    @property
    def 							_UpperCamelCase				(					self   ):
          return ["input_ids", "bbox", "attention_mask", "pixel_values"]






    @property
    def 							_UpperCamelCase				(					self   ):
          warnings.warn(
              """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""      ,_lowerCamelCase      ,)
          return self.image_processor_class






    @property
    def 							_UpperCamelCase				(					self   ):
          warnings.warn(
              """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""      ,_lowerCamelCase      ,)
          return self.image_processor




"""simple docstring"""


from .imports import is_rich_available


if is_rich_available():
	from rich.traceback import install

	install(show_locals=False)

else:
	raise ModuleNotFoundError('To use the rich extension, install rich with `pip install rich`')


